The agricultural industry continuously develops new plant varieties which are designed to produce high yields under a variety of environmental and adverse conditions. At the same time, the industry also seeks to decrease the costs and potential risks associated with traditional approaches such as fertilizers, herbicides and pesticides. In order to meet these demands, plant breeding techniques have been developed and used to produce plants with desirable phenotypes. Such phenotypes may include, for example, increased crop quality and yield, increased crop tolerance to environmental conditions (e.g., drought, extreme temperatures), increased crop tolerance to viruses, fungi, bacteria, and pests, increased crop tolerance to herbicides, and altering the composition of the resulting crop (e.g., sugar, starch, protein, or oil).
To breed plants that exhibit a desirable trait or phenotype, a wide variety of techniques can be employed (e.g., cross-breeding, hybridization, recombinant DNA technology). Many methods have been developed to screen new plant varieties for the appearance of advantageous traits and phenotypes, including hyperspectral image analysis. In this method, a hyperspectral image of a plant is captured and the pixels of each image are analyzed for their spectral properties across a range of wavelengths.
An advantage to hyperspectral image analysis is that, because an entire spectrum is acquired at each pixel in a hyperspectral image data cube, known relationships between spectral signatures and plant health can be assessed over a full plant. However, disadvantages of hyperspectral image analysis include both cost and complexity. Due to the fact that hyperspectral image data cubes are large, multi-dimensional datasets, considerable computing power, sensitive detectors, and large data storage capacities are needed for their analysis. Furthermore, most current methods for discerning “plant pixels” from “non-plant pixels” in a hyperspectral image are performed manually. As such, these methods are slow, tedious and require extensive manpower to complete. Therefore, an improvement in any one of these factors can reduce the high cost, data storage requirements, data transfer time, and manual intervention that are associated with obtaining and processing hyperspectral image data.